Supported Models¶
Scikithts extends the work done by Hyndman in a few ways. One of the most important ones is the ability to use a variety of different underlying modeling techniques to predict the base forecasts.
We have implemented so far 4 kinds of underlying models:
 AutoArima, thanks to the excellent implementation provided by the folks at alkalineml
 SARIMAX, implemented by the statsmodels package
 HoltWinters exponential smoothing, also implemented in statsmodels
 Facebook’s Prophet
The full feature set of the underlying models is supported, including exogenous variables handling. Upon instantiation, use keyword arguments to pass the the arguments you need to the underlying model instantiation, fitting, and prediction.
Note
The main development focus is adding more support underlying models. Stay tuned, or feel free to check out the Contributing guide.
Models¶

class
hts.model.
AutoArimaModel
(node: hts.hierarchy.HierarchyTree, **kwargs)[source]¶ Wrapper class around
pmdarima.AutoARIMA
Variables:  model (pmdarima.AutoARIMA) – The instance of the model
 mse (float) – MSE for insample predictions
 residual (numpy.ndarry) – Residuals for the insample predictions
 forecast (pandas.DataFramer) – The forecast for the trained model

class
hts.model.
SarimaxModel
(node: hts.hierarchy.HierarchyTree, **kwargs)[source]¶ Wrapper class around
statsmodels.tsa.statespace.sarimax.SARIMAX
Variables:  model (SARIMAX) – The instance of the model
 mse (float) – MSE for insample predictions
 residual (numpy.ndarry) – Residuals for the insample predictions
 forecast (pandas.DataFramer) – The forecast for the trained model

class
hts.model.
HoltWintersModel
(node: hts.hierarchy.HierarchyTree, **kwargs)[source]¶ Wrapper class around
statsmodels.tsa.holtwinters.ExponentialSmoothing
Variables:  model (ExponentialSmoothing) – The instance of the model
 _model (HoltWintersResults) – The result of model fitting. See statsmodels.tsa.holtwinters.HoltWintersResults
 mse (float) – MSE for insample predictions
 residual (numpy.ndarry) – Residuals for the insample predictions
 forecast (pandas.DataFramer) – The forecast for the trained model

class
hts.model.
FBProphetModel
(node: hts.hierarchy.HierarchyTree, **kwargs)[source]¶ Wrapper class around
fbprophet.Prophet
Variables:  model (Prophet) – The instance of the model
 mse (float) – MSE for insample predictions
 residual (numpy.ndarry) – Residuals for the insample predictions
 forecast (pandas.DataFramer) – The forecast for the trained model